一种改进的RefineDet多尺度人脸检测方法

An improved RefineDet multi-scale face detection method

针对车站、商场等大型场所中客流量大、背景复杂等原因导致多尺度人脸检测精度低的问题,建立了一种基于RefineDet多层特征图融合的多尺度人脸检测方法。首先利用第一级网络进行特征提取并在不同尺度的特征图上粗略预估人脸位置;然后在第二级中通过特征金字塔网络将低层特征与高层特征融合,进一步增强小尺寸人脸的语义信息;最后,通过置信度和焦点损失函数对检测框进行二次抑制,达到边框的精确回归。实验中将人脸候选区域的宽高比只设置为1:1,以此来降低运算量并提高人脸检测精度。在WiderFace数据集上的实验结果表明,该方法能有效检测不同尺度的人脸,在Easy、Medium、Hard3个子数据集上测试结果分别为93.4%、92%、84.4%的MAP,尤其对小尺寸人脸的检测精度有明显提高。

Aiming at the low precision of multi-scale face detection caused by large passenger flow and complicated background in large places such as stations and shopping malls, a multi-scale face detection method based on RefineDet multi-layer feature map fusion is established. Firstly, the first-level network is used for feature extraction and the face position is roughly predicted on the feature maps of different scales. Then, in the second level, the feature pyramid network is used to fuse the low-level features and the high-level features together to further enhance the semantics of small-sized faces information. Lastly, the detection box is secondarily suppressed by the confidence and focal loss function to achieve accurate return of the border. In the experiment, the aspect ratio between the width and the height of the face candidate region is only set to 1:1 in order to reduce the amount of calculation and improve the face detection accuracy. Experimental results on Wider Face datasets show that the method can effectively detect different scales of human faces, and the test results of MAP(mean average precision) on the three sub-data sets of Easy, Medium and Hard are 93.4%, 92% and 84.4% respectively, in particular, the detection accuracy of small-sized human faces is significantly improved.